# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from fastapi.testclient import TestClient

from sandbox.datasets.types import EvalResult, Prompt, TestConfig
from sandbox.server.online_judge_api import GetPromptByIdRequest, GetPromptsRequest, SubmitRequest
from sandbox.server.server import app

client = TestClient(app)


async def test_provided_data_get():
    row = {'id': 0, 'labels': '{}', 'content': 'this is a prompt'}
    request = GetPromptsRequest(dataset='this doesn\'t matter',
                                config=TestConfig(dataset_type='AutoEvalDataset', provided_data=[row, row]))
    response = client.post('/get_prompts', json=request.model_dump())
    assert response.status_code == 200
    results = [Prompt(**sample) for sample in response.json()]
    assert len(results) == 2
    assert results[0].prompt == row['content']


async def test_provided_data_get_id():
    row = {'id': 0, 'labels': '{}', 'content': 'this cis a prompt'}
    request = GetPromptByIdRequest(dataset='this doesn\'t matter',
                                   id=0,
                                   config=TestConfig(dataset_type='AutoEvalDataset', provided_data=row))
    response = client.post('/get_prompt_by_id', json=request.model_dump())
    assert response.status_code == 200
    result = Prompt(**response.json())
    assert result.prompt == row['content']


async def test_provided_data_submit_passed():
    row = {
        "content":
            "\n有一个名为'Hitters_X_train.csv'的数据集，数据的前两行如下：\n| Unnamed: 0         |   AtBat |   Hits |   HmRun |   Runs |   RBI |   Walks |   Years |   CAtBat |   CHits |   CHmRun |   CRuns |   CRBI |   CWalks |   LeagueN |   DivisionW |   PutOuts |   Assists |   Errors |   NewLeagueN |\n|:-------------------|--------:|-------:|--------:|-------:|------:|--------:|--------:|---------:|--------:|---------:|--------:|-------:|---------:|----------:|------------:|----------:|----------:|---------:|-------------:|\n| -Darryl Strawberry |     475 |    123 |      27 |     76 |    93 |      72 |       4 |     1810 |     471 |      108 |     292 |    343 |      267 |         1 |           0 |       226 |        10 |        6 |            1 |\n| -Glenn Wilson      |     584 |    158 |      15 |     70 |    84 |      42 |       5 |     2358 |     636 |       58 |     265 |    316 |      134 |         1 |           0 |       331 |        20 |        4 |            1 |\n\n问题：\n对球员的辅助次数（Assists）和失误（Errors）进行关联性分析。\n\n回答需要满足以下要求：\n1. 数据文件为'Hitters_X_train.csv'，请严格参考数据的表头，使用python代码进行回答问题。\n2. 使用'pandas.read_csv'来读取数据，默认数据在代码的同一路径下。\n3. 分析代码写成叫'proc_data()'的函数，且该函数不需要任何输入参数，用问题中的相关性系数作为返回值。\n",
        "id":
            3,
        "labels":
            "{\"execution_language\":\"python\", \"fewshot\":\"问题：有一个名为'person.csv'的数据集，数据 schema 如下：\\n\\n+-------------+---------+\\n| Column Name | Type    |\\n+-------------+---------+\\n| id          | int     |\\n| email       | varchar |\\n+-------------+---------+\\n\\n其中 id 是该表的主键列(具有唯一值的列)。该表的每一行包含一封电子邮件。电子邮件将不包含大写字母。\\n \\n使用 python pandas 库编写解决方案 删除 所有重复的电子邮件，只保留一个具有最小 id 的唯一电子邮件。\\n\\n回答需要满足以下要求：\\n1. 数据文件为'person.csv'，请严格参考数据的表头，使用python代码进行回答问题。\\n2. 使用'pandas.read_csv'来读取数据，默认数据在代码的同一路径下。\\n3. 分析代码写成叫'proc_data()'的函数，且该函数不需要任何输入参数，用处理后的 dataframe 作为返回值。\\n\\n答案：\\n```python\\nimport pandas as pd\\n\\ndef proc_data():\\n    # 读取 CSV 文件并创建 DataFrame\\n    df = pd.read_csv('person.csv')\\n\\n    # 将 email 列转换为小写\\n    df['email'] = df['email'].str.lower()\\n\\n    # 根据 email 列进行排序，再根据 id 列进行排序\\n    df.sort_values(['email', 'id'], inplace=True)\\n\\n    # 删除重复的电子邮件，只保留具有最小 id 的唯一电子邮件\\n    df.drop_duplicates(subset='email', keep='first', inplace=True)\\n\\n    return df\\n```\\n\\n----------------\\n\\n问题：有一个名为'animals.csv'的数据集，数据 schema 如下：\\n\\n+-------------+--------+\\n| Column Name | Type   |\\n+-------------+--------+\\n| name        | object |\\n| species     | object |\\n| age         | int    |\\n| weight      | int    |\\n+-------------+--------+\\n\\n编写一个解决方案来列出体重严格超过 100 千克的动物的名称。按体重降序返回动物名称。\\n\\n回答需要满足以下要求：\\n1. 数据文件为'animals.csv'，请严格参考数据的表头，使用python代码进行回答问题。\\n2. 使用'pandas.read_csv'来读取数据，默认数据在代码的同一路径下。\\n3. 分析代码写成叫'proc_data()'的函数，且该函数不需要任何输入参数，用一个 python list 作为返回值。\\n\\n答案：\\n```python\\nimport pandas as pd\\n\\ndef proc_data():\\n    # 读取 CSV 文件并创建 DataFrame\\n    df = pd.read_csv('animals.csv')\\n\\n    # 筛选体重严格超过 100 千克的动物\\n    filtered_df = df[df['weight'] > 100]\\n\\n    # 按体重降序排序\\n    sorted_df = filtered_df.sort_values('weight', ascending=False)\\n\\n    # 获取动物名称列表\\n    animal_names = sorted_df['name'].tolist()\\n\\n    return animal_names\\n```\\n\\n----------------\", \"programming_language\":\"python\", \"task_id\":\"data_analysis_python/1\"}",
        "test":
            "{\"asset\":\"{\\\"Hitters_X_train.csv\\\": null, \\\"Hitters_X_train.csv\\\": \\\""","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","LeagueN","DivisionW","PutOuts","Assists","Errors","NewLeagueN"
"-Darryl Strawberry",475,123,27,76,93,72,4,1810,471,108,292,343,267,1,0,226,10,6,1
"-Glenn Wilson",584,158,15,70,84,42,5,2358,636,58,265,316,134,1,0,331,20,4,1
"-Leon Durham",484,127,20,66,65,67,7,3006,844,116,436,458,377,1,0,1231,80,7,1
"-Tony Gwynn",642,211,14,107,59,52,5,2364,770,27,352,230,193,1,1,337,19,4,1
"-Dave Concepcion",311,81,3,42,30,26,17,8247,2198,100,950,909,690,1,1,153,223,10,1
"-Tom Brookens",281,76,3,42,25,20,8,2658,657,48,324,300,179,0,0,106,144,7,0
"-Tim Laudner",193,47,10,21,29,24,6,1136,256,42,129,139,106,0,1,299,13,5,0
"-Mike Marshall",330,77,19,47,53,27,6,1928,516,90,247,288,161,1,1,149,8,6,1
"-Marty Barrett",625,179,4,94,60,65,5,1696,476,12,216,163,166,0,0,303,450,14,0
"-Buddy Biancalana",190,46,2,24,8,15,5,479,102,5,65,23,39,0,1,102,177,16,0
"-Willie McGee",497,127,7,65,48,37,5,2703,806,32,379,311,138,1,0,325,9,3,1
"-Cal Ripken",627,177,25,98,81,70,6,3210,927,133,529,472,313,0,0,240,482,13,0
"-Mike Schmidt",20,1,0,0,0,0,2,41,9,2,6,7,4,1,0,78,220,6,1
"-Gary Ward",380,120,5,54,51,31,8,3118,900,92,444,419,240,0,1,237,8,1,0
"-Rafael Belliard",309,72,0,33,31,26,5,354,82,0,41,32,26,1,0,117,269,12,1
"-Jim Presley",616,163,27,83,107,32,3,1437,377,65,181,227,82,0,1,110,308,15,0
"-Mookie Wilson",381,110,9,61,45,32,7,3015,834,40,451,249,168,1,0,228,7,5,1
"-Tony Pena",510,147,10,56,52,53,7,2872,821,63,307,340,174,1,0,810,99,18,1
"-Gary Redus",340,84,11,62,33,47,5,1516,376,42,284,141,219,1,0,185,8,4,0
"-Pat Sheridan",236,56,6,41,19,21,5,1257,329,24,166,125,105,0,0,172,1,4,0
"-Steve Lombardozzi",453,103,8,53,33,52,2,507,123,8,63,39,58,0,1,289,407,6,0
"-Darnell Coles",521,142,20,67,86,45,4,815,205,22,99,103,78,0,0,107,242,23,0
"-Larry Sheets",338,92,18,42,60,21,3,682,185,36,88,112,50,0,0,0,0,0,0
"-Bob Melvin",268,60,5,24,25,15,2,350,78,5,34,29,18,1,1,442,59,6,1
"-Dwayne Murphy",329,83,9,50,39,56,9,3828,948,145,575,528,635,0,1,276,6,2,0
"-Graig Nettles",354,77,16,36,55,41,20,8716,2172,384,1172,1267,1057,1,1,83,174,16,1
"-Andres Galarraga",321,87,10,39,42,30,2,396,101,12,48,46,33,1,0,805,40,4,1
"-Gary Matthews",370,96,21,49,46,60,15,6986,1972,231,1070,955,921,1,0,137,5,9,1
"-Rick Manning",205,52,8,31,27,17,12,5134,1323,56,643,445,459,0,0,155,3,2,0
"-George Bell",641,198,31,101,108,41,5,2129,610,92,297,319,117,0,0,269,17,10,0
"-Jody Davis",528,132,21,61,74,41,6,2641,671,97,273,383,226,1,0,885,105,8,1
"-Keith Hernandez",551,171,13,94,83,94,13,6090,1840,128,969,900,917,1,0,1199,149,5,1
"-Julio Franco",599,183,10,80,74,32,5,2482,715,27,330,326,158,0,0,231,374,18,0
"-Carmelo Martinez",244,58,9,28,25,35,4,1335,333,49,164,179,194,1,1,142,14,2,1
"-Tom Paciorek",213,61,4,17,22,3,17,4061,1145,83,488,491,244,0,1,178,45,4,0
"-Lee Lacy",491,141,11,77,47,37,15,4291,1240,84,615,430,340,0,0,239,8,2,0
"-Ozzie Guillen",547,137,2,58,47,12,2,1038,271,3,129,80,24,0,1,261,459,22,0
"-Bill Doran",550,152,6,92,37,81,5,2308,633,32,349,182,308,1,1,262,329,16,1
"-Mike Diaz",209,56,12,22,36,19,2,216,58,12,24,37,19,1,0,201,6,3,1
"-Gary Pettis",539,139,5,93,58,69,5,1469,369,12,247,126,198,0,1,462,9,7,0
"-Ozzie Virgil",359,80,15,45,48,63,7,1493,359,61,176,202,175,1,1,682,93,13,1
"-Kevin Mitchell",328,91,12,51,43,33,2,342,94,12,51,44,33,1,0,145,59,8,1
"-Mike Scioscia",374,94,5,36,26,62,7,1968,519,26,181,199,288,1,1,756,64,15,1
"-John Moses",399,102,3,56,34,34,5,670,167,4,89,48,54,0,1,211,9,3,0
"-Johnny Grubb",210,70,13,32,51,28,15,4040,1130,97,544,462,551,0,0,0,0,0,0
"-Tim Wallach",480,112,18,50,71,44,7,3031,771,110,338,406,239,1,0,94,270,16,1
"-Al Newman",185,37,1,23,8,21,2,214,42,1,30,9,24,1,0,76,127,7,0
"-Harry Spilman",143,39,5,18,30,15,9,639,151,16,80,97,61,1,1,138,15,1,1
"-Terry Kennedy",19,4,1,2,3,1,1,19,4,1,2,3,1,1,1,692,70,8,0
"-Kurt Stillwell",279,64,0,31,26,30,1,279,64,0,31,26,30,1,1,107,205,16,1
"-Hal McRae",278,70,7,22,37,18,18,7186,2081,190,935,1088,643,0,1,0,0,0,0
"-Ozzie Smith",514,144,0,67,54,79,9,4739,1169,13,583,374,528,1,0,229,453,15,1
"-Shawon Dunston",581,145,17,66,68,21,2,831,210,21,106,86,40,1,0,320,465,32,1
"-Tito Landrum",205,43,2,24,17,20,7,854,219,12,105,99,71,1,0,131,6,1,1
"-Buddy Bell",568,158,20,89,75,73,15,8068,2273,177,1045,993,732,1,1,105,290,10,1
"-Bill Buckner",629,168,18,73,102,40,18,8424,2464,164,1008,1072,402,0,0,1067,157,14,0
"-Dan Pasqua",280,82,16,44,45,47,2,428,113,25,61,70,63,0,0,148,4,2,0
"-Juan Beniquez",343,103,6,48,36,40,15,4338,1193,70,581,421,325,0,0,211,56,13,0
"-Kevin Bass",591,184,20,83,79,38,5,1689,462,40,219,195,82,1,1,303,12,5,1
"-Greg Brock",325,76,16,33,52,37,5,1506,351,71,195,219,214,1,1,726,87,3,0
"-Phil Garner",313,83,9,43,41,30,14,5885,1543,104,751,714,535,1,1,58,141,23,1
"-Donnie Hill",339,96,4,37,29,23,4,1064,290,11,123,108,55,0,1,104,213,9,0
"-Ron Roenicke",275,68,5,42,42,61,6,961,238,16,128,104,172,1,0,181,3,2,1
"-Darrell Porter",155,41,12,21,29,22,16,5409,1338,181,746,805,875,0,1,165,9,1,0
"-Juan Samuel",591,157,16,90,78,26,4,2020,541,52,310,226,91,1,0,290,440,25,1
"-Ronn Reynolds",126,27,3,8,10,5,4,239,49,3,16,13,14,1,0,190,2,9,1
"-Garry Templeton",510,126,2,42,44,35,11,5562,1578,44,703,519,256,1,1,207,358,20,1
"-Len Dykstra",431,127,8,77,45,58,2,667,187,9,117,64,88,1,0,283,8,3,1
"-Bruce Bochy",127,32,8,16,22,14,8,727,180,24,67,82,56,1,1,202,22,2,1
"-Wade Boggs",580,207,8,107,71,105,5,2778,978,32,474,322,417,0,0,121,267,19,0
"-Ron Oester",523,135,8,52,44,52,9,3368,895,39,377,284,296,1,1,367,475,19,1
"-Mike Davis",489,131,19,77,55,34,7,2051,549,62,300,263,153,0,1,310,9,9,0
"-Rickey Henderson",608,160,28,130,74,89,8,4071,1182,103,862,417,708,0,0,426,4,6,0
"-Tommy Herr",559,141,2,48,61,73,8,3162,874,16,421,349,359,1,0,352,414,9,1
"-Tom Foley",263,70,1,26,23,30,4,888,220,9,83,82,86,1,0,81,147,4,1
"-Mike Kingery",209,54,3,25,14,12,1,209,54,3,25,14,12,0,1,102,6,3,0
"-Ted Simmons",127,32,4,14,25,12,19,8396,2402,242,1048,1348,819,1,1,167,18,6,1
"-Denny Walling",382,119,13,54,58,36,12,2133,594,41,287,294,227,1,1,59,156,9,1
"-Sid Bream",522,140,16,73,77,60,4,730,185,22,93,106,86,1,0,1320,166,17,1
"-Mitch Webster",576,167,8,89,49,57,4,822,232,19,132,83,79,1,0,325,12,8,1
"-Tony Fernandez",687,213,10,91,65,27,4,1518,448,15,196,137,89,0,0,294,445,13,0
"-Ron Hassey",341,110,9,45,49,46,9,2331,658,50,249,322,274,0,0,251,9,4,0
"-Ray Knight",486,145,11,51,76,40,11,3967,1102,67,410,497,284,1,0,88,204,16,0
"-Dave Henderson",388,103,15,59,47,39,6,2174,555,80,285,274,186,0,1,182,9,4,0
"-Tim Flannery",368,103,3,48,28,54,8,1897,493,9,207,162,198,1,1,209,246,3,1
"-Chili Davis",526,146,13,71,70,84,6,2648,715,77,352,342,289,1,1,303,9,9,1
"-Jeff Reed",165,39,2,13,9,16,3,196,44,2,18,10,18,0,1,332,19,2,1
"-Brett Butler",587,163,4,92,51,70,6,2695,747,17,442,198,317,0,0,434,9,3,0
"-Steve Sax",633,210,6,91,56,59,6,3070,872,19,420,230,274,1,1,367,432,16,1
"-Steve Garvey",557,142,21,58,81,23,18,8759,2583,271,1138,1299,478,1,1,1160,53,7,1
"-Candy Maldonado",405,102,18,49,85,20,6,950,231,29,99,138,64,1,1,161,10,3,1
"-Alex Trevino",202,53,4,31,26,27,9,1876,467,15,192,186,161,1,1,304,45,11,1
"-Joe Carter",663,200,29,108,121,32,4,1447,404,57,210,222,68,0,0,241,8,6,0
"-Rick Schu",208,57,8,32,25,18,3,653,170,17,98,54,62,1,0,42,94,13,1
"-Joel Skinner",315,73,5,23,37,16,4,450,108,6,38,46,28,0,1,227,15,3,0
"-Jose Uribe",453,101,3,46,43,61,3,948,218,6,96,72,91,1,1,249,444,16,1
"-Eddie Murray",495,151,17,61,84,78,10,5624,1679,275,884,1015,709,0,0,1045,88,13,0
"-Don Slaught",314,83,13,39,46,16,5,1457,405,28,156,159,76,0,1,533,40,4,0
"-Paul Molitor",437,123,9,62,55,40,9,4139,1203,79,676,390,364,0,0,82,170,15,0
"-Hubie Brooks",306,104,14,50,58,25,7,2954,822,55,313,377,187,1,0,116,222,15,1
"-Rance Mulliniks",348,90,11,50,45,43,10,2288,614,43,295,273,269,0,0,60,176,6,0
"-Dan Gladden",351,97,4,55,29,39,4,1258,353,16,196,110,117,1,1,226,7,3,0
"-Craig Reynolds",313,78,6,32,41,12,12,3742,968,35,409,321,170,1,1,106,206,7,1
"-Lou Whitaker",584,157,20,95,73,63,10,4704,1320,93,724,522,576,0,0,276,421,11,0
"-Howard Johnson",220,54,10,30,39,31,5,1185,299,40,145,154,128,1,0,50,136,20,1
"-Chris Bando",254,68,2,28,26,22,6,999,236,21,108,117,118,0,0,359,30,4,0
"-Rey Quinones",312,68,2,32,22,24,1,312,68,2,32,22,24,0,0,86,150,15,0
"-Eric Davis",415,115,27,97,71,68,3,711,184,45,156,119,99,1,1,274,2,7,1
"-Phil Bradley",526,163,12,88,50,77,4,1556,470,38,245,167,174,0,1,250,11,1,0
"-Reggie Jackson",419,101,18,65,58,92,20,9528,2510,548,1509,1659,1342,0,1,0,0,0,0
"-Wayne Tolleson",475,126,3,61,43,52,6,1700,433,7,217,93,146,0,1,37,113,7,0
"-Jose Cruz",479,133,10,48,72,55,17,7472,2147,153,980,1032,854,1,1,237,5,4,1
"-Doug DeCinces",512,131,26,69,96,52,14,5347,1397,221,712,815,548,0,1,119,216,12,0
"-Dave Parker",637,174,31,89,116,56,14,6727,2024,247,978,1093,495,1,1,278,9,9,1
"-Bob Dernier",324,73,4,32,18,22,7,1931,491,13,291,108,180,1,0,222,3,3,1
"-Alvin Davis",479,130,18,66,72,76,3,1624,457,63,224,266,263,0,1,880,82,14,0
"-Jesse Barfield",589,170,40,107,108,69,6,2325,634,128,371,376,238,0,0,368,20,3,0
"-Vance Law",360,81,5,37,44,37,7,2268,566,41,279,257,246,1,0,170,284,3,1
"-Will Clark",408,117,11,66,41,34,1,408,117,11,66,41,34,1,1,942,72,11,1
"-Len Matuszek",199,52,9,26,28,21,6,805,191,30,113,119,87,1,1,235,22,5,1
"-Ken Landreaux",283,74,4,34,29,22,10,3919,1062,85,505,456,283,1,1,145,5,7,1
"-Dale Sveum",317,78,7,35,35,32,1,317,78,7,35,35,32,0,0,45,122,26,0
"-Mel Hall",442,131,18,68,77,33,6,1416,398,47,210,203,136,0,0,233,7,7,0
"-Scott Bradley",220,66,5,20,28,13,3,290,80,5,27,31,15,0,1,281,21,3,0
"-Herm Winningham",185,40,4,23,11,18,3,524,125,7,58,37,47,1,0,97,2,2,1
"-Dale Murphy",614,163,29,89,83,75,11,5017,1388,266,813,822,617,1,1,303,6,6,1
"-Kevin McReynolds",560,161,26,89,96,66,4,1789,470,65,233,260,155,1,1,332,9,8,1
"-Bob Kearney",204,49,6,23,25,12,7,1309,308,27,126,132,66,0,1,419,46,5,0
"-Tim Hulett",520,120,17,53,44,21,4,927,227,22,106,80,52,0,1,70,144,11,0
"-Ryne Sandberg",627,178,14,68,76,46,6,3146,902,74,494,345,242,1,0,309,492,5,1
"-Mike Heath",288,65,8,30,36,27,9,2815,698,55,315,325,189,1,0,259,30,10,0
\\\"}\", \"code\":\"#<INSERT>\\n\\nanswer = proc_data()\\nassert round(answer, 3) == 0.642\\n    \"}"
    }
    canonical_solution = "\nimport pandas as pd\n\ndef proc_data():\n    # Load the data from the uploaded CSV file\n    hitters_data = pd.read_csv('Hitters_X_train.csv')\n\n    # Calculate the Pearson correlation coefficient between \"Assists\" and \"Errors\"\n    correlation = hitters_data[\"Assists\"].corr(hitters_data[\"Errors\"])\n    \n    return correlation"
    request = SubmitRequest(dataset='this doesn\'t matter',
                            id=0,
                            config=TestConfig(dataset_type='AutoEvalDataset', provided_data=row),
                            completion=f'''
```python
{canonical_solution}
```
''')
    response = client.post('/submit', json=request.model_dump())
    assert response.status_code == 200
    result = EvalResult(**response.json())
    assert result.accepted == True

    request.config.extra['append_flag'] = True
    response = client.post('/submit', json=request.model_dump())
    assert response.status_code == 200
    result = EvalResult(**response.json())
    assert result.accepted == True
    print(result)
